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In recent years, the construction industry worldwide has shown significant interest in Building Information Modeling (BIM). This study aims to analyze the dissemination of knowledge about BIM in construction engineering applications using Main Path Analysis (MPA). The research sample comprises 3,761 papers related to BIM's application in the construction industry, sourced from the ISI Web of Science database. Initially, we investigate trends in paper publications, conduct country and journal analyses, and examine author statistics. Subsequently, we calculate traversal counts along the search path links to reveal the development trajectory of BIM. The trajectory of BIM's evolution in the construction industry can be divided into four stages as identified through the global key-route main path analysis: 1) BIM standardization; 2) Integration of completed building projects using BIM; 3) BIM applications in precast construction projects; and 4) BIM applications in land management. These findings provide a clear understanding of how BIM has been applied and evolved within the construction industry. [ABSTRACT FROM AUTHOR]

The capitalisation on and transfer of technological, engineering and scientific knowledge associated with empirical know-how is an important issue for the sustainability and development of manufacturing. Indeed, certain sectors of industry are facing the increasing ageing of the labour force, recruitment difficulties and high staff turnover, leading to a loss of knowledge and know-how. In a context of numerical and digital transition and the migration of processes to industry 4.0, one of major challenges manufacturers face today is their capacity to build intelligent platforms for acquiring, storing and transferring their know-how and knowledge. It is crucial to create new media and tools for staff training and development capable of capturing knowledge and reusing it to create a project history through expertise and data collection. This paper presents the methodology and guidelines for implementing electronic knowledge books (eK-Books), along with their uses. The eK-Book is a semantic web-based hypertext medium (channel) allowing stakeholders to capitalise on, structure and transfer knowledge by using concept maps, process maps, influence graphs, downloadable documents, web pages and hypermedia knowledge sheets. They are intended for engineers, expert or novice technicians, manufacturers, sector coordinators and plant managers, as well as trainers and learners. They are usable and manageable in all types of environments and with different levels of accessibility. This paper highlights (1) the transfer knowledge capacity of eK-Books and (2) their usability in two agri-food sectors namely (1) the cheese sector with protected designation of origin (PDO) and protected geographical indication (PGI), and (2) the butchery and cold meat sectors. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal "Materials" focused on the topic of surface engineering for wear, corrosion, and erosion prevention in metallic and ceramic materials. The special issue includes twelve scientific papers covering various aspects of the operational performance and properties of metallic and ceramic materials. The papers discuss topics such as the effects of peening treatments, corrosion resistance, tribological behavior, and wear improvement through microstructural modification and the deposition of wear-resistant coatings. The special issue received significant interest from authors and readers, leading to the announcement of a second volume on the same topic. The summary acknowledges the need for experimental data in surface engineering to understand the wear and performance of engineering materials. [Extracted from the article]

Technological advancements have made rock engineering more data-driven, leading to increased use of machine learning (ML). While the use of ML in rock engineering has the potential to transform the industry, several methodological issues should first be addressed: (i) rock engineering's use of biased (poor quality) data, resulting in biased ML models and (ii) limited rock mass classification and characterization data. If these issues are not addressed, rock engineering risks using unreliable ML models that can have potential real-life adverse impacts. This paper aims to provide an overview of these methodological issues and demonstrate their impact on the reliability of ML models using surrogate models. To take full advantage of the benefits of ML, rock engineers should make sure that their ML models are reliable by ensuring that there are sufficient unbiased data to develop reliable ML models. In the context of this paper, the term sufficient retains a relative meaning since the amount of data that is sufficient to develop reliable a ML models depends on the problem under consideration and the application of the ML model (e.g., pre-feasibility, feasibility, design stage). [ABSTRACT FROM AUTHOR]

Descriptive geometry has indispensable applications in many engineering activities. A summary of these is provided in the first chapter of this paper, preceded by a brief introduction into the methods of representation and mathematical recognition related to our research area, such as projection perpendicular to a single plane, projection images created by perpendicular projection onto two mutually perpendicular image planes, but placed on one plane, including the research of curves and movements, visual representation and perception relying on a mathematical approach, and studies on toothed driving pairs and tool geometry in order to place the development presented here among them. As a result of the continuous variability of the technological environment according to various optimization aspects, the engineering activities must also be continuously adapted to the changes, for which an appropriate approach and formulation are required from the practitioners of descriptive geometry, and can even lead to improvement in the field of descriptive geometry. The imaging procedures are always based on the methods and theorems of descriptive geometry. Our aim was to examine the spatial variation in the wear of the tool edge and the machining of the components of toothed drive pairs using two cameras. Resolving contradictions in spatial geometry reconstruction research is a constant challenge, to which a possible answer in many cases is the searching for the right projection direction, and positioning cameras appropriately. A special method of enumerating the possible infinite viewpoints for the reconstruction of tool surface edge curves is presented in the second part of this paper. In the case of the monitoring the shape geometry, taking into account the interchangeability of the projection directions, i.e., the property of symmetry, all images made from two perpendicular directions were taken into account. The procedure for determining the correct directions in a mathematically exact way is also presented through examples. A new criterion was formulated for the tested tooth edge of the hob to take into account the shading of the tooth next to it. The analysis and some of the results of the Monge mapping, suitable for the solution of a mechanical engineering task to be solved in a specific technical environment, namely defining the conditions for camera placements that ensure reconstructibility are also presented. Taking physical shadowing into account, conclusions can be drawn about the degree of distortion of the machined surface from the spatial deformation of the edge curve of the tool reconstructed with correctly positioned cameras. [ABSTRACT FROM AUTHOR]

The X-structure has been used for vibration isolation due to its nonlinear characteristic. Here, the use of an inerter-based X-structure vibration isolator is proposed to improve the dynamic performance of the traditional X-structure vibration isolator by exploiting the mass amplification characteristic of the inerter while satisfying the lightweight design requirement. Based on the arrangement of the inerter and X-structure, transverse-mounted, series-connected and parallel-connected inerter-based X-structure vibration isolators are designed. The relevant dynamic equations for these isolators are established using the Lagrange principle, their dynamic responses are obtained using the harmonic balance method and pseudoarc-length method, and their isolation performances are evaluated by three performance indices and compared with those of the X-structure and traditional linear vibration isolators. The results show that the transverse-mounted inerter-based X-structure vibration isolator has nonlinear inertial, nonlinear conservative forcing and nonlinear stiffness characteristics and that the series-connected and parallel-connected inerter-based X-structure vibration isolators have linear inertial and nonlinear stiffness characteristics. The transverse-mounted and parallel-connected inerter-based X-structure vibration isolators have a smaller relative dynamic displacement peak, absolute dynamic displacement peak, absolute displacement transmissibility peak and resonance frequency and wider isolation frequency band than the X-structure isolator, and only the high-frequency absolute displacement transmissibility is larger for the transverse-mounted and parallel-connected inerter-based X-structure vibration isolators. A series-connected inerter-based X-structure vibration isolator can isolate a relatively low-frequency vibration because of the antiresonance frequency and thus can improve the high-frequency absolute displacement transmissibility, while the relative dynamic displacement peak, absolute dynamic displacement peak and absolute displacement transmissibility peak are larger for the series-connected inerter-based X-structure vibration isolator than for the X-structure isolator. The parallel-connected inerter-based X-structure vibration isolator exhibits the best isolation performance and the transverse-mounted inerter-based X-structure vibration isolator exhibits the best shock performance among the four X-structure and linear vibration isolators studied. In addition, the experimental prototype of the inerter-based X-structure vibration isolator is built to test its performance, the theoretical and experimental results are compared to verify the validity of the theoretical modelling. This paper verifies the benefit of introducing the inerter in the X-structure and offers a better choice for effective vibration isolation in practical engineering. [ABSTRACT FROM AUTHOR]

The traditional approach to formulating building codes often is slow and labor-intensive, and may struggle to keep pace with the rapid evolution of technology and domain findings. Overcoming such challenges necessitates a methodology that streamlines the modernization of codal provisions. This paper proposes a machine learning (ML) approach to append a variety of codal provisions, including those of empirical, statistical, and theoretical natures. In this approach, a codal provision (i.e., equation) is analyzed to trace its properties (e.g., engineering intuition and causal logic). Then a ML model is tailored to preserve the same properties and satisfy a collection of similarity and performance measures until declared equivalent to the provision at hand. The resulting ML model harnesses the predictive capabilities of ML while arriving at predictions similar to the codal provision used to train the ML model, and hence it becomes possible to use in lieu of the codal expression. This approach was examined successfully for seven structural engineering phenomena contained within various building codes, including those in North America and Australia. The findings suggest that the proposed approach could lay the groundwork for implementing ML in the development of future building codes. [ABSTRACT FROM AUTHOR]

This study demonstrates the effect of nitrogen doping on the surface state densities (Nss) of monolayer MoS2 and its effect on the responsivity and the response time of the photodetector. Our experimental results shows that by doping monolayer MoS2 by nitrogen, the surface state (Nss) increases thereby increasing responsivity. The mathematical model included in the paper supports the relation of photocurrent gain and its dependency on trap level which states that the increasing the trap density increases the photocurrent gain and the same is observed experimentally. The experimental results at room temperature revealed that nitrogen doped MoS2 have a high NSS of 1.63 X 1013 states/m2/eV compared to undoped MoS2 of 4.2 x 1012 states/m2/eV. The increase in Nss in turn is the cause for rise in trap states which eventually increases the value of photo responsivity from 65.12 A/W (undoped MoS2) to 606.3 A/W (nitrogen doped MoS2). The response time calculated for undoped MoS2 was 0.85 sec and for doped MoS2 was 0.35 sec. Finally, to verify the dependence of surface states on the responsivity, the surface states were varied by varying temperature and it was observed that upon increment in temperature, the surface states decreases which causes the responsivity values also to decrease. [ABSTRACT FROM AUTHOR]

Complex systems are difficult to manage, operate and maintain. This is why we see teams of highly specialised engineers in industries such as aerospace, nuclear and subsurface. Condition based monitoring is also employed to maximise the efficiency of extensive maintenance programmes instead of using periodic maintenance. A level of automation is often required in such complex engineering platforms in order to effectively and safely manage them. Advances in Artificial Intelligence related technologies have offered greater levels of automation but this potentially pivots the weight of decision making away from the operator to the machine. Implementing AI or complex algorithms into a platform can mean that the Operators' control over the system is diminished or removed altogether. For example, in the Boeing 737 Air Max Disaster, AI had been added to a platform and removed the operators' control of the system. This meant that the operator could not then move outside the extremely reserved, algorithm defined, "envelope" of operation. This paper analyses the challenges of AI driven condition based monitoring where there is a potential to see similar consequences to those seen in control engineering. As the future of society becomes more about algorithm driven technology, it is prudent to ask, not only whether we should implement AI into complex systems, but how this can be achieved ethically and safely in order to reduce risk to life. [ABSTRACT FROM AUTHOR]

A large number of post-tensioned concrete bridges were built in the second half of the last century. They have often been insufficiently maintained during their lifetime (usually around 50 years). Nowadays, these structures exhibit significant deterioration, mainly due to leakages and also due to various other deficiencies such as a small concrete cover. Their load-bearing capacity needs to be verified. This paper focuses on estimating the load-bearing capacity calculation of existing post-tensioned concrete bridges. In the engineering practice, this is carried out using the partial factor method according to the currently valid standards (Czech standards ČSN and the Eurocodes), which often impose more stringent requirements than the original standards. The partial factor method then often leads to low load-bearing capacities. This study deals with the bridge for which a very low load-bearing capacity has been determined. For this reason, a comparative probabilistic analysis was performed, allowing for a better description of the uncertainties in the resistance and load effect variables. The probabilistic approach appears to be less conservative and yields a higher load-bearing capacity. [ABSTRACT FROM AUTHOR]

The paper is concerned with the possibilities of long-term monitoring of constructions using modern wireless sensors and systems. There are currently many constructions under long-term monitoring performed by modern sensors. The monitoring enables an assessment of the current health of the construction or it can be used to recalculate and adjust the BMI models and subsequently evaluate the static and dynamic resistance of the construction. The sensors and systems installed enable the detection of hidden defects or newly emerging failures and damage. Using the results obtained from the monitoring of the construction, it is possible to predict and carry out a timely repair or maintenance of the construction, which will ultimately decrease the financial and time-related costs, ensure greater safety and prolong its lifetime and resistance. [ABSTRACT FROM AUTHOR]

This paper aims to unify q-derivative/q-integrals and h-derivative/h-integrals into a single definition, called q − h -derivative/ q − h -integral. These notions are further extended on the finite interval [ a , b ] in the form of left and right q − h -derivatives and q − h -integrals. Some inequalities for q − h -integrals are studied and directly connected with well known results in diverse fields of science and engineering. The theory based on q-derivatives/q-integrals and h-derivatives/h-integrals can be unified using the q − h -derivative/ q − h -integral concept. [ABSTRACT FROM AUTHOR]

This work is the extended version of a paper presented at the conference HMM2021, about the history of mechanical engineering. First, the initial cultural and industrial steps in the robotic field in Japan are introduced, to display the beginning of this interlaced path, before WW2; then, in the context of the aftermaths of the war, some famous anime heroes are presented as ancestors of the coming mecha anime series. The rising research in the field of robotics and more generally the developments of contemporary Japanese automation industry are then drawn, as a technological substrate for mecha conception. In particular, Masahiro Mori studies are taken into consideration to identify an actual robot design pattern to apply to the following comparative description and examination of the different mecha; finally, thanks to this analysis, the common breeding ground whence robotics and mecha (and their reciprocal influences) arise, is outlined. [ABSTRACT FROM AUTHOR]

The model-based robust approach is successfully applied to different industrial processes for improving their quality performance characteristics. The Robust engineering approach is implemented in cases of heteroscedasticity of the performance characteristics in presence of errors in the process parameters or noise factors, which is typical for the production processes. Higher quality and reproducibility of the quality characteristics can be achieved both for existing production processes or for technological process design at the production planning and development stages. In this paper the model-based robust engineering approach is implemented for the quality improvement of polymethyl methacrylate (PMMA) resist profiles, obtained by electron beam lithography process. Specific technological requirements concerning the developed profile structures, their variances (reproducibility) in production conditions and robustness toward the errors in the process parameters, are considered. Different multicriteria optimization approaches were applying and compared, aiming to simultaneously fulfil the requirements for the geometric characteristics of the positive resist PMMA. [ABSTRACT FROM AUTHOR]

This paper presents a novel and powerful population-based metaheuristic algorithm called Nizar Optimization Algorithm (NOA). This algorithm is based on two techniques. The first technique is to use effective mappings, which are divided into two types: mixing and transformation mappings. The mixing mappings return a mixed vector by replacing or shuffling the elements of two selected vectors, and the transformation mappings return the translation, dilation, or transfer of a selected vector. The second technique is to determine the effective points by using the effective mappings and individuals of the population, and then, these points are used in the learning process of NOA. To validate and demonstrate the performance of the proposed algorithm and its ability to balance exploration and exploitation, NOA is tested on 60 unconstrained benchmark functions and four classic constrained real-world engineering problems. The experimental results are verified by a comparative study with over 20 well-known and recently developed optimization algorithms. These results show that the proposed algorithm outperforms all other algorithms in terms of solution accuracy, convergence curve speed, statistical measurements, and computational expenses. [ABSTRACT FROM AUTHOR]

This research paper presents the Principal Component Analysis (PCA) technique, and its practical application to machine learning. The objective is to address the problem of dimensional reduction or data compression. Based on an intuitive analysis, this document will help bring economists and other professionals in the social sciences closer to these ideas, which are generally alien to their discussions. [ABSTRACT FROM AUTHOR]

Offset boosting is a crucial passage for chaotic signal modification in chaos-based engineering. Searching an offset controller for a 3D chaotic system is usually complex, let alone two independent nonbifurcating offset constants. This paper constructs a class of chaotic systems, providing a single constant posing direct offset boosting for two dimensions. This offset boostable chaotic system regime has multiple typical control modes, including a system variable single control, synchronous common control, reverse control, and differential control. This new type of chaotic systems also finds two-dimensional offset boosting combined with amplitude control. [ABSTRACT FROM AUTHOR]

In this paper, a unified gas kinetic particle (UGKP) method is developed for radiative transfer in both absorbing and anisotropic scattering media. This numerical method is constructed based on our theoretical work on the model reduction for an anisotropic scattering system. The macroscopic solver of this method directly solves the macroscopic anisotropic diffusion equations, eliminating the need to solve higher-order moment equations. The reconstruction of macroscopic scattering source in the microscopic solver, based on the multiscale equivalent phase function we proposed in this work, has also been simplified as one single scattering process, significantly reducing the computational costs. The proposed method has also the property of asymptotic preserving. In the optically thick regime, the proposed method solves the diffusion limit equations for an anisotropic system. In the optically thin regime, the kinetic processes of photon transport are simulated. The consistency and efficiency of the proposed method have been validated by numerical tests in a wide range of flow regimes. The novel equivalent scattering source reconstruction can be used for various transport processes, and the proposed numerical scheme is widely applicable in high-energy density engineering applications. [ABSTRACT FROM AUTHOR]

[Display omitted] • Ru nanoparticles (NPs) anchored on heterointerface of 1 T-MoS 2 and carbon paper (Ru@1T-MoS 2 /CFP) is designed. • The Ru 2 @MoS 2 -85 %/CFP presents superb HER performance with overpotential of 19 mV and a low Tafel slope of 29 mV dec–1. • The strong interaction between 1 T-MoS 2 and OH* makes OH* migrate from Ru to MoS 2 , alleviating the poisoning of Ru sites. • Ru NPs close to the interface of 1 T-MoS2/CFP can achieve an optimal hydrogen adsorption energy for high HER performance. Ru is considered as highly efficient Pt-free catalyst in hydrogen evolution reaction (HER) due to its fast water dissociation kinetics. However, its strong affinity to OH* and H* greatly limits its HER performance. Herein, Ru nanoparticles (NPs) anchored on the mixed substrates of MoS 2 (different 1T contents) and carbon fiber paper (Ru@MoS 2 -1T%/CFP) is designed and synthesized with three kinds of interfaces (MoS 2 /CFP, Ru/CFP, and Ru/MoS 2) as excellent electrocatalyst for HER. Particularly, the Ru 2 @MoS 2 -85%/CFP presents superb HER performance with overpotential of 19 mV at 10 mA cm−2, a low Tafel slope of 29 mV dec–1 and stability of up to 10 days, outperforming reported Ru-based catalysts and Pt/C. The experimental and DFT studies disclose that the hydroxyl migration-assisted dissociation of water and interface-engineering desorption of H 2 process, occurring at the mutually hybridized interface sites of Ru/MoS 2 /CFP, simultaneously speed Volmer/Tafel processes. Compared to Ru, the stronger interaction of 1T-MoS 2 to OH* makes OH* migrate from Ru to MoS 2 , alleviating the poisoning of Ru sites. Also, only Ru NPs close to the interface of 1T-MoS 2 /CFP can achieve an optimal hydrogen adsorption energy for high HER performance. This designed strategy provides a novel idea for preparing supported Ru catalysts for excellent HER performance. [ABSTRACT FROM AUTHOR]

Nature- and society-inspired metaheuristic algorithms have recently become the most promising technological model. To solve more complex optimization problems and complicated engineering applications, this paper proposes a new people duality psychological tendency and feedback mechanism-based Inherited Optimization Algorithm(IOA), which is inspired by people showing positive-negative duality cognitive tendency and adaptive feedback behavior when selecting information resources with different identity attributes. The IOA algorithm contains the symmetric two exploration phases. The exploitation phase adaptively regulates the dualistic psychological balance of people in inheriting the information resources with better existence value through a feedback regulation mechanism controlled by the profitability awareness to increase population diversity. This paper qualitatively and quantitatively evaluates the optimization performance of IOA on 84 benchmarks, including swarm convergence behavior, effectiveness, convergence, robustness, and significance. The scalability of the IOA is investigated using the CEC2017 suites. The algorithm performance in solving constrained optimization is verified on 8 engineering problems. All statistical results of the IOA are compared with the most promising 12 metaheuristics, which shows that the absolute computational efficiency of IOA on four types of functions is 95%, 96.67%, 80.95%, and 76.92%, respectively, the average rank (rank sum ratio) of IOA is 1.08 (1.19%) among the 13 algorithms, ranking first. The Wilcoxon signed rank test results on the CEC2017 suites show that IOA contains 1437 significance indicators out of 1440 comparisons, with the proportion of significant differences 99.79%, which suggests the proposed IOA maintains efficient search efficiency. [ABSTRACT FROM AUTHOR]

[Display omitted] • Main principles and methods for measuring acoustic cavitation are discussed. • Acoustic wave and system parameters affecting US process efficiency are discussed. • US for agri-food applications improved product quality and extraction efficiency. • US can be combined with conventional agri-food processes to improve operations. • Innovative designs for large-scale US assisted food processing are described. Acoustic cavitation, an intriguing phenomenon resulting from the interaction of sound waves with a liquid medium, has emerged as a promising avenue in agri-food processing, offering opportunities to enhance established processes improving primary production of ingredients and further food processing. This comprehensive review provides an in-depth analysis of the mechanisms, design considerations, challenges and scale-up strategies associated with acoustic cavitation for agri-food applications. The paper starts by elucidating the fundamental principles of acoustic cavitation and its measurement, delving then into the diverse effects of different parameters associated with, the acoustic wave, mechanical design and operation of the ultrasonic system, along with those related to the food matrix. The technological advancements achieved in the design and set-up of ultrasonic reactors addressing limitations during scale up are also discussed. The design, engineering and mathematical modelling of ultrasonic equipment tailored for agri-food applications are explored, along with strategies to maximize cavitation intensity and efficiency in the application of brining, freezing, drying, emulsification, filtration and extraction. Advanced US equipment, such as multi-transducers (tubular resonator, FLOW:WAVE®) and larger processing surface areas through innovative designing (Barbell horn, CascatrodesTM), are one of the most promising strategies to ensure consistency of US operations at industrial scale. This review paper aims to provide valuable insights into harnessing acoustic cavitation's potential for up-scaling applications in food processing via critical examination of current research and advancements, while identifying future directions and opportunities for further research and innovation. [ABSTRACT FROM AUTHOR]

There is a pressing requirement to design a simple battery thermal model that balances simulation accuracy regarding battery thermal characteristics and engineering application feasibility. The paper carries out the experimental study and model characterization of thermoelectric coupling characteristics of ternary lithium batteries with different shapes under ambient temperature conditions. The paper designs a characterization method of the battery thermoelectric coupling model. It conducts experiments to analyze the battery's charging and discharging characteristics and extracts essential modeling parameters. It reveals the differences in the modeling parameters of ternary lithium batteries with different shapes and constructs the thermoelectric coupling models of the square and 21700 cylindrical ternary lithium battery respectively. At the same time, the paper applies the model to carry out the simulation and experimental verification of the battery thermal behavior. The experimental data monitored by the infrared thermal imager and temperature sensors are compared with the simulation results. The errors are less than 5 %. It can provide a reference for related researches and engineering applications of battery thermal behavior and thermal management. • Designing a model characterization method for batteries with different shapes. • Revealing differences in modeling parameters of batteries with different shapes. • Constructing a simpler battery thermal model with low calculation volume. • Balancing the accuracy of simulation with the feasibility of applications. [ABSTRACT FROM AUTHOR]

Resilient performance is a crucial characteristic of complex socio-technical systems, enabling them to sustain essential functionality during changing or stressful conditions. Resilience Engineering (RE), a sub-field of safety research, focuses on this perspective of resilience. RE emphasises its "cornerstone model", presenting the RE system goals of "anticipating, monitoring, responding and learning". The cornerstone of learning remains fragmented and undertheorized in the existing literature. This paper aims to enrich RE research and its practical implications by developing a nuanced and comprehensive understanding of the role of learning from resilient performance. To achieve this aim, a scoping literature review was conducted to assess how learning is conceptualised in the RE literature and the theoretical foundations on which previous work rest. The main findings show that RE researchers view learning as the process of understanding the system, sharing knowledge, and re-designing system properties. The application of established learning theories is limited. This paper contributes to research by proposing an organisational process for the RE cornerstone of learning, paving the way for deeper discussions in future studies about learning from resilient performance within complex socio-technical systems. • Organisational learning is a core requirement for the safety and optimal functionality of complex socio-technical systems. • Conceptualizations of organisational learning from resilient performance are under-developed. • The literature review shows that learning from resilient performance is fragmented and undertheorized. • The research lays groundwork for future discussions on learning from resilient performance • A refined organisational process for learning within RE, grounded in established learning theory, is proposed. [ABSTRACT FROM AUTHOR]

In this paper, the Ru/RuS 2 nanoparticles as dual co-catalysts were self-assembled on the surface of g-C 3 N 4 nanotubes (Ru/RuS 2 /g-C 3 N 4 NTs) for photocatalytic H 2 production. The Ru/RuS 2 /g-C 3 N 4 NTs showed greatly enhanced photocatalytic H 2 production activity (1409 μmol·g−1·h−1), 1.16 times of RuS 2 /g-C 3 N 4 NTs (1212 μmol·h−1·g−1), 10.51 times of Ru/g-C 3 N 4 NTs (134 μmol·g−1·h−1), and 82.88 times of the pure g-C 3 N 4 NTs (17 μmol·g−1·h−1). Besides, the apparent quantum yield value (AQE) of Ru/RuS 2 /g-C 3 N 4 NTs is 3.92% at 370 nm. Ru/RuS 2 as dual co-catalysts are self-assembled on the surface of g-C 3 N 4 NTs and show strong electronic synergistic interaction between the interfaces, reduce Δ G H* of RuS 2 and desorption energy of Ru, and promote the selectivity and activity of HER kinetically and thermodynamically respectively, which exhibits higher photogenerated carrier concentration and lower charge migration resistance than RuS 2 and Ru, showing the synergistic effect to facilitate the generation and migration of carriers and provides active sites for photocatalysis. [Display omitted] • Ru/RuS 2 nanoparticles as dual co-catalysts are self-assembled on the surface of g-C 3 N 4 nanotubes. • Ru/RuS 2 /g-C 3 N 4 NTs show greatly enhanced photocatalytic H 2 production activity (1409 μmol·g−1·h−1). • Ru/RuS 2 show strong electronic synergistic interaction between the interfaces. • Ru/RuS 2 promote the selectivity and activity of HER kinetically and thermodynamically respectively. [ABSTRACT FROM AUTHOR]

• By controlling the concentration of Li-ions, the phase composition of high entropy oxide (HEO) can be regulated. • A high concentration of point defects (i.e., oxygen vacancies) and extended defects (such as dislocations and stacking faults) can be introduced into the spinel phase. • Successful control over defect engineering in spinel-rocksalt dual-phase HEO has been achieved through phase-selective regulation. • The incorporation of an intergrown interface improves the conductivity and structural stability of electrode materials. This paper presents the effectiveness of phase-selective regulation in the Mg-Co-Ni-Mn-Zn-O-based spinel-rocksalt dual-phase high entropy oxide (HEO) for achieving novel electrochemical properties. The key factor distinguishing spinel from rocksalt in response to Li-ion incorporation is the separation of ions at tetrahedral sites, which offers greater structural adjustability and enables incoordinate defect regulation of individual phases. The optimized Li incorporation facilitates a high concentration of point defects (specifically oxygen vacancies) and appropriately extended defects (such as dislocations and stacking fault) in the spinel phase, accompanied by a spinel-rocksalt intergrown interface. These exquisite adjustments improve the conductivity, structural stability, and specific capacity of the HEO electrode material. Nevertheless, the ever-increasing Li content triggers the transformation from a spinel-rocksalt dual-phase HEO to a single-phase rocksalt HEO, resulting in serious lattice distortion and excessive defects that hinder the efficient transport of Li-ion, ultimately leading to unsatisfactory electrochemical performance. Thus, phase-selective engineering facilitates the determination of appropriate defects that synergistically interact with the intergrown interface and the dual-phase structure. The optimized HEO20 exhibits outstanding electrochemical performance, with a high initial discharge capacity of 2049.8 mAh g−1 at 0.1C and a high-rate capability of 502.9 mAh g−1 at 2C. Moreover, it demonstrates remarkable battery capacity retention of 99.5% after 1300 cycles at 0.5C and a stable cycle performance of 717.3 mAh g−1. [ABSTRACT FROM AUTHOR]

This paper examines the seismic response of buckling restrained braced frames (BRBFs) under near-field (NF) ground motions to obtain the interrelationships among different engineering demand parameters (EDPs). Several case study BRBFs with varying span lengths and heights are designed based on relevant US design standards. These study frames are numerically developed in the OpenSees platform, incorporating the applicable nonlinear characteristics of the pertinent structural components. Nonlinear time response analyses are conducted on the adopted frames under a suite of NF ground motion records, and the results are post-processed to obtain structural responses in the form of EDPs such as inter-storey drift ratio (IDR), residual drift ratio (RDR), maximum ductility demand (μ max), and cumulative ductility demand (μ cum). Regression relationships among EDPs are also explored. Subsequently, probabilistic seismic demand models are developed, and the resulting fragility curves are generated. The results from the study can display the approximate regression relationships among the adopted EDPs under NF records. Identifying RDR as the critical EDP that governs the BRBF response under NF seismic excitation, distinct damage state (DS) values are also proposed for the IDR, μ max, and μ cum to facilitate fragility curve development. The study also shows that the increase in span length makes the BRBFs more vulnerable to permanent damage under NF disturbances. • Examines near-field seismic response of buckling-restrained braced frames factoring in bay span length & frame height. • For the accounted scenario, the inter-relation among the different engineering demand parameters (EDPs) of BRBFs, namely, inter-storey drift ratio (IDR), residual drift ratio (RDR), maximum buckling-restrained brace (BRB) ductility (μ max)and cumulative BRB ductility (μ cum) are derived. • Additionally, the associated median estimate of capacity (Sc) of IDR, μ max and μ cum are obtained based on the code given RDR median capacity value and the nonlinear time response analyses results. • Finally, the resulting fragility curves for RDR, IDR μ max and μ cum are generated and discussed. [ABSTRACT FROM AUTHOR]

Navigating the complex pavement system to the circular economy is a multi-dimensional and time-consuming endeavour. Obstructed by lock-in mechanisms of the prevailing "take-make-dispose" linear mode, the current circular economy studies in pavement infrastructure often focus only on one certain type of practice, featured by fragmentation from a system perspective. This study presents the first systematic literature review on the historical development trend of the circular economy concept in the pavement sector based on the collected 59 studies from 2006 to 2022. This review also identifies the soft (regulatory, social/environmental) and hard (technical, market/economic) drivers of- and barriers to the paradigm shift, aiming to coordinate the existing fragmented findings and facilitate the future systemic implementation of the circular economy. The synthesis of the results indicates that the underdeveloped technical guidance and legal system are inhibiting the paradigm shift to the circular economy. Furthermore, the authors argue that the importance of transparent and standardized data along the whole supply chain should be highlighted. Ultimately, this paper suggests that a welcoming environment set by the road authorities is crucial for innovation and entrepreneurship, as well as for constructing a system where stakeholders can confidently take responsibility and initiative. • First systematic review of circular economy transition in pavement engineering. • Identify the soft and hard drivers of- and barriers to the sustainable transition. • Guide towards the future adoption of material passports in the pavement sector. • Call for decentralization and multi-stakeholder engagement in the decision-making process. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries age continuously during usage due to their characteristics and the influence of various external factors, but as degradation deepens, it can lead to an apparent decrease in battery safety and reliability. Therefore, predicting remaining useful life from the current to end state and preventing possible dangerous incidents are essential for battery health management. A novel method from the perspective of providing aging features reference and balance prognostics speed and precision is proposed. Firstly, the used dataset is preprocessed in many ways. Then, through feature engineering, 79 features are extracted in the dataset from three perspectives: direct, evolution, statistics, and most representative 16 features are filtered to form the final feature set based on the method of combining discretization and importance. Feature engineering is established to find crucial information within the dataset that correlates highly with remaining life and represents most battery recession paths. Finally, the sparse autoencoder and Transformer integrated approach is proposed to build the life prediction model, and it can learn the temporal relationship between the feature set and remaining cycles quickly and accurately. According to the forecasting results of 62 batteries with different aging conditions, the error can reach 7.43% by only using the feature sequence under 30 cycle lengths. For early life prediction, the error of using the first 100 cycles data is at most 2.6%. • A novel aging feature filtering approach is proposed based on feature engineering. • A prediction model is established by using a sparse autoencoder and Transformer. • More accurate RUL and early life of a battery can be predicted by using fewer cycle data. • After the feature engineering presented in this paper, the final aging feature set selected has strong applicability. [ABSTRACT FROM AUTHOR]

• K.S Bfr can store Cd2+ in protein cavities in vitro. • The engineering bacteria with K.S Bfr can enhance the removal of Cd2+ from water. • Bacterioferritin up-regulates the ribosomal metabolic pathway. Cadmium can enter water through various industrial pollutants, posing serious threats to the ecological environment and human health. This study unveiled an effective method for removing Cd2+ from water by introducing bacterioferritin, known as Candidatus Kuenenia stuttgartiensis (K.S Bfr), into engineering bacteria (EB) to facilitate metal ion removal via K.S Bfr storage. Additionally, the paper explores the mechanism behind Cd2+ removal by EB using K.S Bfr. The binding kinetics revealed that EB's Cd2+ binding capacity was significantly higher than that of wild bacteria, and Cd2+ was found to enter the protein cavity of K.S Bfr in vitro. When exposed to Cd2+ concentrations ranging from 0 to 2.5 mM, EB demonstrated a survival rate above 70 %, whereas wild bacteria's survival rate remained below 50 %. The superior Cd2+ removal capacity of EB can be attributed to the intracellular storage capacity of K.S Bfr. Tandem Mass Tags quantitative proteomics analysis showed that EB mitigated cytotoxicity mainly by up-regulating the expression of binding, homeostasis, efflux, and ribosome-related proteins. K.S Bfr was identified as a major pathway for bacteria to remove Cd2+ since it provided independent storage space for Cd2+ and ensured intracellular metal ion homeostasis. This study presents an effective strategy for treating metal wastewater, offering promising new applications for bioremediation. [ABSTRACT FROM AUTHOR]

This paper reports a new knowledge-driven engineered feature learning approach in response to the Global AI Challenge for Building E&M Facilities held by the Electrical and Mechanical Service Department (EMSD) of the Hong Kong SAR. The results were awarded with a Grand Prize by the competition organizer. A dynamically engineered multi-modal feature learning (DEMMFL) method is proposed for predicting the cooling load of two office buildings. The DEMMFL model is estimated with the Lasso-ridge regression and compared with other well-known methods such as the Lasso. The novel approach applies control system knowledge to engineer useful features and explore load patterns for multi-mode modeling. Deep learning methods including LSTM, GRU, and AutoGluon are implemented for automated machine learning and tested in parallel to compare the performance of the proposed model with existing methods. The proposed model is demonstrated to predict long-term cooling load most accurately using engineered features from weather information only. • A new dynamic feature engineering method for office building cooling load prediction. • Slow and fast dynamic features engineered from available weather data only. • Control-knowledge based feature to learn on-hours dynamic responses. • Multi-modal approach with eight day-type features to learn occupancy loads. • Deep recurrent learning and AutoML methods compared to the proposed method. [ABSTRACT FROM AUTHOR]

Titanium dioxide (TiO 2)-based photocatalysts have gained increasing attention for their versatile applications in organic degradation, hydrogen production, air purification, and CO 2 reduction. Various TiO 2 -based heterojunction structures, including type I, type II, Schottky junction, Z-scheme, and S-scheme, have been extensively studied. The current research frontier is centered on the engineering modifications of TiO 2 -based nanoheterojunction photocatalysts, such as defect engineering, morphological engineering, crystal phase/facet engineering, and multijunction engineering. These modifications enhance carrier transport, separation, and light absorption, thereby improving the photocatalytic performance. Remarkably, this aspect has been less addressed in existing reviews. This review aims to fill this gap by focusing on the engineering modifications of TiO 2 -based nanoheterojunction photocatalysts. We delve into specific topics like oxygen vacancies, n-p homojunctions, and double defects. The review also systematically discusses the applications of multidimensional heterojunctions and examines carrier transport pathways in heterophase/facet junctions and their interactions with heterojunctions. A comprehensive summary of multijunction systems, including multi-Schottky junctions, semiconductor-based heterojunction-attached Schottky junctions, and multisemiconductor-based heterojunctions, is presented. Lastly, we outline future perspectives in this promising research field. This paper will assist researchers in constructing more efficient TiO 2 -based nanoheterojunction photocatalysts. [Display omitted] • Oxygen vacancies can affect the energy band and control the valence state. • The synergy of double defects can facilitate photogenerated charge separation. • Three-dimensional heterojunctions possess several different configurations. • Heterophase/facet junctions can assist heterojunctions to promote photocatalysis. • The multijunction system has multiple photogenerated carrier transfer paths. [ABSTRACT FROM AUTHOR]